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Early Skin Cancer Detection Using Deep learning 3

Early Skin Cancer Detection Using Deep Learning

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ali, Mohd Arif Khan, Munna Sherwani, Kashif I.K. Jamia Millia Islamia Department of Electrical Engineering New Delhi India

ISBN: (纸本)9798350319125

Skin cancer is a prevalent and potentially life-threatening disease which is increasing day by day globally, due to the depletion of the ozone layer and other atmosphere changes. Various computer-aided methods have been proposed for diagnosis of skin cancer. However, for accurate diagnosis of skin cancer, computer-aided diagnosis (CAD) system poses several difficulties. These includes data quality, heterogeneity and complexity of skin layers which is a challenging visual characteristic for cell detection. Challenge of automatically and effectively extracting discriminative features for skin cancer detection is indeed a significant research problem in the field of medical image analysis. Deep learning-based methods have shown great promise in automating feature extraction by learning relevant features directly from the raw images. Convolutional Neural Networks (CNNs) and other deep architectures can automatically discover hierarchical features that may not be apparent to human experts. However, these models require a large amount of labeled data for training, which can be a challenge for the skin cancer detection. To address these challenges and further improve deep learning models for skin cancer detection. The architecture of Alex Net offers an innovative approach by proposing a Convolutional Neural Network (CNN) based Alex net model. This technique involves pre-trained weights and features learned by Alex net model on large datasets. Additionally for accurate skin cancer diagnosis, data augmentation techniques are employed to enhance the robustness and generalization capabilities of the model. The proposed model achieves 98.0% accuracy with augmentation and without any augmentation it achieves 92.9% accuracy. © 2023 IEEE.

关键词： Alex Net CNN transfer learning model Deep learning ISIC dataset Malignant detection

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Research on Cooperative Strategy Guidance Based on Game Theory 3

Research on Cooperative Strategy Guidance Based on Game Theo...

引用

3rd international conference on Machine learning and Computer Application, ICMLCA 2022

作者： Wang, Xin Wang, Xinghua Li, Xu Lu, Yao School of Equipment Engineering Shenyang Li Gong University Shenyang110000 China

ISBN: (纸本)9781510664814

In modern war, the continuous development of missile technology makes the air combat environment more complex. With the emergence of intelligent mobile targets, the interception and guidance of missiles are facing severe challenges. It not only requires the continuous improvement of the precision guidance performance of missiles, but also requires multiple missiles to work together to maximize the effectiveness of group warfare. In this paper, a new method of missile interception guidance strategy based on game theory is proposed, which is based on dynamic game and matrix game in game theory. By establishing a many to one missile target game model, the strategy set of missile and target is determined. Then, according to the many to one missile target game model, the system equation is determined, and the cooperative guidance strategy is obtained through the calculation results. Finally, the Nash equilibrium solution of the dynamic game model is obtained by using the model prediction algorithm. And a cooperative guidance strategy suitable for many to one interception is obtained. The simulation results verify the effectiveness of the guidance law. © 2023 SPIE.

关键词： Game theory

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Motivational Design Framework of Online French Interpretation Teaching Model Based on intelligent Big data 3rd

Motivational Design Framework of Online French Interpretatio...

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wang, Shanshan Xi’an Fanyi University Shaanxi Xi’an710000 China

ISBN: (纸本)9783031631320

As the demand for big data computing grows, the processing speed of clusters needs to be rapidly improved. However, the processing performance of the current big data processing framework has gradually been unable to meet this rapidly growing demand. Since the storage architecture of the cluster is distributed storage, the storage of data in the big data processing process becomes one of the factors that affect the processing performance of the cluster. When non-native speakers speak in English, they will show different accents or the characteristics of non-English native accents. Based on this feature, the speaker’s accent and its native language can be identified. This paper uses smart big data to design current French online teaching activities It is necessary to carry out motivation design in the network teaching activity design, and put forward the development direction of the framework of how to carry out the motivation design in the network teaching activity design. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Motivation

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An Efficiency way to analyse Diabetic Retinopathy Detection and Classification using Deep learning Techniques 3

An Efficiency way to analyse Diabetic Retinopathy Detection ...

引用

3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Reddy, K Sudhakar Narayanan, M. Malla Reddy University School of Engineering Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798350399264

Lesions develop on the retina of the eyes as a result of the progressive eye condition known as diabetic retinopathy (DR), which is brought on by type-2 diabetes. In especially for the working-age population in sustainable nations, it is thought that Diabetes Retinopathy is the main cause of blindness in diabetes patients. The aim of the treatment appears to be to maintain the patient's degree of eyesight because the issue is chronic. Diabetic retinopathy must be accurately identified in order to fully protect the patient's vision. The major issue with DR detection is manual diagnosis, which is time-consuming, expensive, and labor-intensive. A retinal scan of the patient's eyes must also be evaluated by an ophthalmologist as part of the treatment. The latter also appears to be more challenging, particularly in the early phases of the ailment when sickness indications are less obvious in the photos. Early detection of diabetic retinopathy has become easier because of deep learning algorithms, and images of the retinal fundus (DR) may now be analysed using machine learning. There are various stages of diabetic retinopathy, and the early stages are symptomless. Ophthalmologists can spot some retinal issues, but they can't always determine their root causes or stages of development. Ophthalmologists advise retina specialists to treat disease as a result. Bayesian neural networks (BNNs) had been used to benchmark the binary categorization of diabetic retinopathy as referable or non-referable in the current system. We suggest developing a Convolution neural network (CNN) and data analysis method to categorise diabetic retinopathy based on clinical data, predicting whether the patient is diabetic or not and identifying its stage with estimation, employing measurements are needed to maximise the intended performance measure with different datasets and clinical lesion images. © 2023 IEEE.

关键词： Recurrent neural networks

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Improving Sign Language Recognition for Deaf and Hard of Hearing Individuals using the VGG16 Model 3

Improving Sign Language Recognition for Deaf and Hard of Hea...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Benjamin, Minu Inba Shanthini Watson Patan, Yaseen Kumar, Ansilin S.M. Reddy, Pocharam Nithyanandh Ahamad, Syed Faheez Puneeth, Muvvala Sai Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331532420

Persons who are deaf and who cannot speak face a variety of challenges, which emphasises the need for greater research to offer appropriate assistance. Interaction and communication with others are a big challenge. Since sign language is the major means of communication for these people, there is a significant knowledge gap among hearing individuals when it comes to understand and interpret sign language. As a result, there has been a notable expansion in the field of sign language study to close this gap. This work highlights the importance of sign language detection and offers a thorough analysis of relevant studies carried out in this domain using deep learning technique. It approaches the issue from multiple perspective, including translation, sign language recognition, and access to relevant datasets. Understanding these facets is intended to create a meaningful impact on sign language literature and its practical implementation. VGG16 deep learning model for sign language recognition offers the best solution for solving communication challenges faced by deaf and muted individuals. The results show that the VGG16 based technique achieves accuracy of around 95.8%. The VGG16 model uses a Convolutional Neural Network (CNN) architecture, which applies many layers of convolutional filters to identify patterns and attributes in the input images. Recognizing sign language using VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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Recyclable and Non-Recyclable Waste Recognition using Image Processing with Machine learning: Recycle Mate

Recyclable and Non-Recyclable Waste Recognition using Image ...

引用

intelligent Technologies (CONIT), international conference on

作者： Amit Nara Smit Panchal Prof. Suchita Dange Department of Computer Engineering A.P. Shah Institute of Technology Thane India

Waste Management is the effort needed to make our world a better place. As solid waste may cause a problem for our surroundings. Nowadays many people don't even know how to distinguish between recyclable and non-recyclable objects, we know only some general things which are recyclable like paper, plastic, cardboard and drinking cans. To deal with this problem a deep machine-learning Convolution Neural Network (CNN) was created to help us all with an achievable waste management task. The automated task works for everyone as it lets down the burden of work from many people’s shoulders. Our model is built using the Keras API. The model categorizes the data into a binary class. It has been tested with many objects which gave an acceptable prediction accuracy.

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Secure data Reduplication System with Efficient and Reliable Multi-Key Management in Cloud Storage 3

Secure Data Reduplication System with Efficient and Reliable...

引用

3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Jayaram, B. Suguna, T. Fairooz, Shaik Arul Selvam, P. Vimal, S. Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Malla Reddy Engineering College Department of Cse -AIML Telangana Secunderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350336238

Malicious hackers may compromise hundreds or millions of machines with little effort, allowing them to shut down networks, steal sensitive data, and compromise critical infrastructure. data is stolen and used for malicious purposes by hackers who exploit security flaws in the network. And the sophisticated technology they use, like the bucks they do, are continually feeding them the information they needed, not just once. Moreover, even if Internet firewalls have improved, cybercrime has been on the rise. Then, we have a long way to go before we approach the limit, what with all the different kinds of cloud server devices that can connect to the internet nowadays. It's certain that in the future, little devices, no matter how helpful they are really in our everyday lives, will become the preferred tool of villains. In this research, we offer a novel security technique for protecting sensitive data on cloud servers. This intelligent cloud server was protected from any hacking with the aid of this algorithm. The system's output was measured against those of pre-existing algorithms. More than 96% of inputs are authenticated by the proposed technique, while password cracking operations are reduced by about 3%. The source code security management is improved to 99% and the updating issues for various devices entries is reduced 0.02%, and further the privacy breach is reduced up to 0.32%. Hence the proposed method was more secured from cyber security issues. © 2023 IEEE.

关键词： Sensitive data

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Big data Analytics for Electrical Systems using Machine learning Algorithms

Big Data Analytics for Electrical Systems using Machine Lear...

引用

Technology, engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： Salava V Satyanarayana Pillalamarri Madhavi Electrical and Electronics Engineering Hyderabad Institute of Technology and Management Hyderabad India

Big data Analytics plays a vital role in every electrical industry to analyze the large amount of data received from their dispatch centers. Typically, data retrieved from electrical systems like Generation, Transmission, and distribution will be analyzed using many control systems like SCADA and HMI without any human intervention. To analyze this data as per industry 4.0, Every system should be automated by integrating the data into the internet of things along with cyber security systems. With consideration of industry 4.0 standards, in view of effective optimized operational maintenance of electrical systems in the future, this paper provides intelligent predicted data by analyzing current and existing data that is fetching from electrical systems i.e., Various state electrical utility data files. The available data of electrical systems can be analyzed by using various Supervised Machine learning algorithms and this paper verified the accuracy of various machine learning algorithms by analyzing the predicted data by each algorithm.

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Deep learning based Chatbot Architecture for Medical Diagnosis and Treatment Recommendation 3

Deep Learning based Chatbot Architecture for Medical Diagnos...

引用

3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Rajani, Girish Ruparel, Khushi Thadomal Shahani Engineering College Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350348347

With the exponential growth and rapid development in the fields of deep learning and neural networks, chatbots have gained a lot of popularity and have become a proven, and efficient tool to interact and provide service to users. Healthcare is one of the most promising fields where chatbots can be used more efficiently. This has become important, especially in the current medical landscape, where there is a shortage of doctors, and patients often have to wait long periods before getting any medical guidance. By using the power of transformer models and machine learning algorithms chatbots can help patients with personalized diagnoses and treatment recommendations, efficiently at ease and convenience. This helps the patients to access medical services anywhere, at any point in time. This paper proposes a well-planned systematic architecture for a medical chatbot that utilizes the potential of transformers, classification algorithms and machine-learning models. The architecture includes three main components: a Naïve Bayes Classifier, a Binary Tree classifier along with a Support Vector Classifier, and a sequence-to-sequence model. These algorithms are used to classify symptoms and determine the severity of a medical condition to provide patients with accurate medical diagnoses and treatment recommendations. Overall, the proposed architecture is built and designed to bridge the gap between doctors and patients by providing immediate access to medical advice, making it a promising tool for improving the quality and accessibility of healthcare services. © 2023 IEEE.

关键词： Diagnosis

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Particle Swarm Optimization for Efficient data Dissemination in VANETs 1

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3rd international conference on Artificial-Business Analytics, Quantum and Machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Kumar, Arvind Dixit, Prashant Tyagi, S.S. Manav Rachna International Institute of Research and Studies Faridabad India IIMT College of Engineering Greater Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

Vehicular Ad-Hoc Networks (VANETs) are wireless networks that use vehicles as nodes, and they have become increasingly important due to growing concerns about urban traffic congestion. Reliable and safe data dissemination in VANET networks requires effective optimization methods that balance competing performance metrics. The meta-heuristic technique known as Particle Swarm Optimization (PSO) can achieve efficient optimization by creating multiple paths through Time delay-based Multipath Routing (TMR) and finding the best routes. In a recent study, PSO was compared to other optimization methods, including Ant Colony Optimization (ACO) and Firefly Optimization (FFO), and demonstrated considerable enhancements in various metrics, including increased throughput, decreased packet-loss ratio, reduced end to end delay, and lower routing overhead-ratio. These results highlights potential of PSO-based approaches to improve data dissemination in VANETs and address the challenges of urban traffic congestion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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